Understanding Mechanisms of Cardiac Resynchronization Therapy Response to Improve Patient Selection and Outcomes

Waks, Jonathan W.; Alday, Erick Andres Perez; Tereshchenko, Larisa G.

doi:10.1161/circep.118.006290

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…It has been previously shown that increasing degrees of interventricular (rather than intraventricular) dyssynchrony is expected to result in improved rates of clinical CRT response. 30 Previous analysis of the SMART-AV study showed that optimally timed AV delay provides an incremental benefit to the substantial interventricular conduction delay 7, 9 , suggesting that both LV lead and right ventricular (RV) lead placement should target maximizing RV-LV delay. Pre-procedural planning may involve expensive and time-consuming cardiac imaging.…”

Section: Discussionmentioning

confidence: 99%

Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy: the SMART-AV Study

Howell

Stivland

Steín

et al. 2020

Preprint

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Background: There is a controversy whether the response of both sexes to cardiac resynchronization therapy (CRT) is similar. Optimal CRT delivery requires procedure planning. Objective: To apply machine learning (ML) to develop a prediction model for CRT response. Methods: Participants from the SmartDelay Determined AV Optimization (SMART-AV) trial (n=741; age, 66 ± 11 yrs; 33% female; 100% NYHA III-IV; 100% EF≤35%) were randomly split into training & testing (80%; n=593), and validation (20%; n=148) samples. The entropy balancing procedure was used to match for the means of 30 covariates in male and female groups. Baseline clinical, ECG, echocardiographic and biomarker characteristics, and left ventricular (LV) lead position (43 variables) were included in 6 ML models (random forests, convolutional neural network, lasso, adaptive lasso, plugin lasso, elastic net, ridge, and logistic regression). A composite of freedom from death and heart failure hospitalization and a >15% reduction in LV end-systolic volume index at 6-months post-CRT was the endpoint. Results: The primary endpoint was met by 337 patients (45.5%). Weighting resulted in a perfect balance of means of covariates in men and women. After reweighting, CRT response for women versus men was similar (OR 1.53; 95%CI 0.88-2.65; P=0.131). The adaptive lasso model was more accurate than class I ACC/AHA guidelines criteria (AUC 0.759; 95%CI 0.678-0.840 versus 0.639; 95%CI 0.554-0.722; P<0.0001), well-calibrated, and parsimonious (19 predictors; nearly half are potentially modifiable). Conclusions: After balancing for covariates, both sexes similarly benefit from CRT. ML predicts short-term CRT response and thus may help with CRT procedure planning.

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Section: Discussionmentioning

confidence: 99%

Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy: the SMART-AV Study

Howell

Stivland

Steín

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…It has been previously shown that increasing degrees of interventricular (rather than intraventricular) dyssynchrony is expected to result in improved rates of clinical CRT response. 30 Previous analysis of the SMART-AV study showed that optimally timed AV delay provides an incremental benefit to the substantial interventricular conduction delay 7,9 , suggesting that both LV lead and right ventricular (RV) lead placement should target maximizing RV-LV delay. Preprocedural planning may involve expensive and time-consuming cardiac imaging.…”

Section: Prediction Of Composite Crt Response In a Short-term -Perspe...mentioning

confidence: 99%

Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy

Howell

Stivland

Steín

et al. 2021

JACC: Clinical Electrophysiology

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“…31, 32 Our RCT showed that aCRT reduced RV uncoupling by significantly changing VEU, thereby reducing interventricular dyssynchrony, a known critical predictor of CRT response. 33, 34 In the previous studies, VEU was strongly associated with clinical outcomes. 26 The magnitude of change in VEU has been shown to be the primary driver of acute hemodynamic CRT response.…”

Section: Discussionmentioning

confidence: 86%

Adaptive Cardiac Resynchronization Therapy Effect on Electrical Dyssynchrony (aCRT-ELSYNC): a randomized controlled trial

Haq

Rogovoy

Thomas

et al. 2020

Preprint

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Introduction: Adaptive cardiac resynchronization therapy (aCRT) is known to have clinical benefits over conventional CRT; however, their effects on the electrical dyssynchrony have not been compared. Methods: We conducted a double-blind, randomized controlled trial in patients receiving CRT for routine clinical indications. Participants underwent cardiac computed tomography and 128-electrodes body surface mapping. We measured electrical dyssynchrony on the epicardial surface using noninvasive electrocardiographic imaging (ECGI) before and 6 months post-CRT. Ventricular electrical uncoupling (VEU) was calculated as the difference between the mean left ventricular (LV) and right ventricular (RV) activation times. An electrical dyssynchrony index (EDI) was computed as the standard deviation of local epicardial activation times. Results: We randomized 27 participants (mean age 64 ± 12 y; 34% female; 53% ischemic cardiomyopathy; LV ejection fraction 28 ± 8%; QRS duration 155 ± 21 ms; strict left bundle branch block (LBBB) in 13%). In atypical LBBB (n=11;41%) with S-waves in V5-V6, conduction block occurred in the anterior RV, as opposed to the interventricular groove in those who met the strict LBBB criteria. As compared to baseline, VEU reduced post-CRT in aCRT (median reduction 18.9(interquartile range 4.3-29.2 ms; P=0.034), but not in conventional CRT (21.4(-30.0 to 49.9 ms; P=0.525) group. There were no differences in the degree of change in VEU and EDI indices between treatment groups. Conclusion: The effect of aCRT and conventional CRT on electrical dyssynchrony is largely similar. Further studies are needed to investigate if atypical LBBB with prominent S wave in V5-V6 responds to His bundle pacing

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Understanding Mechanisms of Cardiac Resynchronization Therapy Response to Improve Patient Selection and Outcomes

Cited by 5 publications

References 20 publications

Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy: the SMART-AV Study

Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy: the SMART-AV Study

Using Machine-Learning for Prediction of the Response to Cardiac Resynchronization Therapy

Adaptive Cardiac Resynchronization Therapy Effect on Electrical Dyssynchrony (aCRT-ELSYNC): a randomized controlled trial

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